Graphical fault tree analysis for fatal falls in the construction industry

The current study applied a fault tree analysis to represent the causal relationships among events and causes that contributed to fatal falls in the construction industry. Four hundred and eleven work-related fatalities in the Taiwanese construction industry were analyzed in terms of age, gender, experience, falling site, falling height, company size, and the causes for each fatality. Given that most fatal accidents involve multiple events, the current study coded up to a maximum of three causes for each fall fatality. After the Boolean algebra and minimal cut set analyses, accident causes associated with each falling site can be presented as a fault tree to provide an overview of the basic causes, which could trigger fall fatalities in the construction industry. Graphical icons were designed for each falling site along with the associated accident causes to illustrate the fault tree in a graphical manner. A graphical fault tree can improve inter-disciplinary discussion of risk management and the communication of accident causation to first line supervisors.     

Accidents in the construction industry in the Netherlands: An analysis of accident reports using Storybuilder

As part of an ongoing effort by the Ministry of Social Affairs and Employment of the Netherlands, a research project is being undertaken to construct a causal model for occupational risk. This model should provide quantitative insight into the causes and consequences of occupational accidents. One of the components of the model is a tool to systematically classify and analyse reports of past accidents. This tool ‘Storybuilder’ was described in earlier papers. In this paper, Storybuilder is used to analyse the causes of accidents reported in the database of the Dutch Labour Inspectorate involving people working in the construction industry. Conclusions are drawn on measures to reduce the accident probability. Some of these conclusions are contrary to common beliefs in the industry.     

Critical factors and paths influencing construction workers’ safety risk tolerances

While workers’ safety risk tolerances have been regarded as a main reason for their unsafe behaviors, little is known about why different people have different risk tolerances even when confronting the same situation. The aim of this research is to identify the critical factors and paths that influence workers’ safety risk tolerance and to explore how they contribute to accident causal model from a system thinking perceptive. A number of methods were carried out to analyze the data collected through interviews and questionnaire surveys. In the first and second steps of the research, factor identification, factor ranking and factor analysis were carried out, and the results show that workers’ safety risk tolerance can be influenced by four groups of factors, namely: (1) personal subjective perception; (2) work knowledge and experiences; (3) work characteristics; and (4) safety management. In the third step of the research, hypothetical influencing path model was developed and tested by using structural equation modeling (SEM). It is found that the effects of external factors (safety management and work characteristics) on risk tolerance are larger than that of internal factors (personal subjective perception and work knowledge & experiences). Specifically, safety management contributes the most to workers’ safety risk tolerance through its direct effect and indirect effect; while personal subjective perception comes the second and can act as an intermedia for work characteristics. This research provides an in-depth insight of workers’ unsafe behaviors by depicting the contributing factors as shown in the accident causal model developed in this research.     

Storybuilder—A tool for the analysis of accident reports

As part of an ongoing effort by the ministry of Social Affairs and Employment of The Netherlands a research project is being undertaken to construct a causal model for the most commonly occurring scenarios related to occupational risk. This model should provide quantitative insight in the causes and consequences of occupational accidents. The results should be used to help selecting optimal strategies to reduce these risks taking the costs of accidents and of measures into account. The research is undertaken by an international consortium under the name of Workgroup Occupational Risk Model. One of the components of the model is a tool to systematically classify and analyse past accidents. This tool: “Storybuilder” and its place in the Occupational Risk Model (ORM) are described in the paper.The paper gives some illustrations of the application of the Storybuilder, drawn from the study of ladder accidents, which forms one of the biggest single accident categories in the Dutch data.     

Toward an understanding of the impact of production pressure on safety performance in construction operations

It is not unusual to observe that actual schedule and quality performances are different from planned performances (e.g., schedule delay and rework) during a construction project. Such differences often result in production pressure (e.g., being pressed to work faster). Previous studies demonstrated that such production pressure negatively affects safety performance. However, the process by which production pressure influences safety performance, and to what extent, has not been fully investigated. As a result, the impact of production pressure has not been incorporated much into safety management in practice. In an effort to address this issue, this paper examines how production pressure relates to safety performance over time by identifying their feedback processes. A conceptual causal loop diagram is created to identify the relationship between schedule and quality performances (e.g., schedule delays and rework) and the components related to a safety program (e.g., workers’ perceptions of safety, safety training, safety supervision, and crew size). A case study is then experimentally undertaken to investigate this relationship with accident occurrence with the use of data collected from a construction site; the case study is used to build a System Dynamics (SD) model. The SD model, then, is validated through inequality statistics analysis. Sensitivity analysis and statistical screening techniques further permit an evaluation of the impact of the managerial components on accident occurrence. The results of the case study indicate that schedule delays and rework are the critical factors affecting accident occurrence for the monitored project.     

Assessing the validity of road safety evaluation studies by analysing causal chains

This paper discusses how the validity of road safety evaluation studies can be assessed by analysing causal chains. A causal chain denotes the path through which a road safety measure influences the number of accidents. Two cases are examined. One involves chemical de-icing of roads (salting). The intended causal chain of this measure is: spread of salt --> removal of snow and ice from the road surface --> improved friction --> shorter stopping distance --> fewer accidents. A Norwegian study that evaluated the effects of salting on accident rate provides information that describes this causal chain. This information indicates that the study overestimated the effect of salting on accident rate, and suggests that this estimate is influenced by confounding variables the study did not control for. The other case involves a traffic club for children. The intended causal chain in this study was: join the club --> improve knowledge --> improve behaviour --> reduce accident rate. In this case, results are rather messy, which suggests that the observed difference in accident rate between members and non-members of the traffic club is not primarily attributable to membership in the club. The two cases show that by analysing causal chains, one may uncover confounding factors that were not adequately controlled in a study. Lack of control for confounding factors remains the most serious threat to the validity of road safety evaluation studies.     

Exposure and experience are a confounded nuisance in research on driver behaviour

Road accidents are usually the outcome of multicausal interface problems. Statistical controls therefore need to be equally complex, if accident analyses are to produce understanding of causal factors and lead to correction of contributory errors in driver behaviour. Crude control of accident data for distance travelled, time of day, etc. may identify high-risk groups. However, such controls could produce misleading results from research on individual differences in liability to error, because driving is basically self-paced and purposeful. Certain individuals will thus exhibit characteristically raised levels of risk exposure within the driving task, often as a result of pressures extrinsic to the traffic system. If the errors made by these individuals are to be identified, accident data must be corrected for such self-induced risk exposure, instead of this factor merely being used to “explain” accidents. This need seems most acute in experimental studies, where self-imposed demands and purposes of driving change over time and effects of exposure and experience are thus statistically confounded in accident data. However, control for self-induced risk exposure also seems important in studies of professional drivers' accidents and its neglect calls into question one of the basic assumptions of the “induced exposure” method of interpreting accident data.     

Scenario analysis of freight vehicle accident risks in Taiwan

This study develops a quantitative risk model by utilizing Generalized Linear Interactive Model (GLIM) to analyze the major freight vehicle accidents in Taiwan. Eight scenarios are established by interacting three categorical variables of driver ages, vehicle types and road types, each of which contains two levels. The database that consists of 2043 major accidents occurring between 1994 and 1998 in Taiwan is utilized to fit and calibrate the model parameters. The empirical results indicate that accident rates of freight vehicles in Taiwan were high in the scenarios involving trucks and non-freeway systems, while; accident consequences were severe in the scenarios involving mature drivers or non-freeway systems. Empirical evidences also show that there is no significant relationship between accident rates and accident consequences. This is to stress that safety studies that describe risk merely as accident rates rather than the combination of accident rates and consequences by definition might lead to biased risk perceptions. Finally, the study recommends using number of vehicle as an alternative of traffic exposure in commercial vehicle risk analysis. The merits of this would be that it is simple and thus reliable; meanwhile, the resulted risk that is termed as fatalities per vehicle could provide clear and direct policy implications for insurance practices and safety regulations.     

The systematic integration of human factors into safety analyses: An integrated engineering approach

The performance of the human reliability analysis (HRA) and integration of its outcomes into quantitative risk assessment schemes remains quite a difficult and complex task to perform. Even worse is the assessment of organisational reliability assessment. The reasons of this difficulty mainly lay on the absence of a generically accepted paradigm that enables engineers to include systematically human and organisational factors (H&OF) into the analysis. Broadly speaking, engineering approaches very often account for error of omission forgetting the errors of commission (EOC), and, on top of that, they do not make any macro distinction between pre- and post-initiating human failures. This paper offers a paradigm on how to integrate H&OF into safety analysis by means of the recursive operability analysis (ROA), which has been adapted to accommodate H&OF, and renamed integrated recursive operability analysis (IROA). By means of a practical example, the method will illustrate how to account for H&OF in a systematic and consistent manner using an engineering approach. The paper will even provide a paradigm for the construction of integrated fault trees consistent with the IROA framework.     

Graphical Identifiability Criteria for Total Effects in Studies with an Unobserved Response Variable

Consider a case where cause-effect relationships between variables can be described as a causal diagram and the corresponding Gaussian linear structural equation model. In order to identify total effects in studies with an unobserved response variable, this paper proposes graphical criteria for selecting both covariates and variables caused by the response variable. The results enable us not only to judge from the graph structure whether a total effect can be expressed through the observed covariances, but also to provide its closed-form expression in case where its answer is affirmative. The graphical criteria of this paper are helpful to infer total effects when it is difficult to observe a response variable.

     

Optimal diversification for R&;D project portfolios

Diversification of R&D projects not only can reduce overall risk, but also can create value-enhancement effect. A useful guideline for optimal diversification of R&D projects is important to R&D organizations. This paper extends financial portfolio analyses for R&D management particularly incorporating the technology risk. This study uses a survival model to describe the technology risk since termination of an R&D project can be caused by any technology risk factors. A formula of optimal R&D resource allocation that can dynamically achieve the greatest diversification effect is offered. Furthermore we provide an alternative method for estimating correlations between R&D portfolios, which has a critical influence on diversification effect. The method can be useful in risk assessment when measure the exposure of R&D portfolio to particular sources of uncertainty. The evaluation framework for R&D portfolios optimization also can be applied in project-selection decisions.     

Estimating the risk of collisions between bicycles and motor vehicles at signalized intersections

Collisions between bicycles and motor vehicles have caused severe life and property losses in many countries. The majority of bicycle-motor vehicle (BMV) accidents occur at intersections. In order to reduce the number of BMV accidents at intersections, a substantial understanding of the causal factors for the collisions is required. In this study, intersection BMV accidents were classified into three types based on the movements of the involved motor vehicles and bicycles. The three BMV accident classifications were through motor vehicle related collisions, left-turn motor vehicle related collisions, and right-turn motor vehicle related collisions. A methodology for estimating these BMV accident risks was developed based on probability theory. A significant difference between this proposed methodology and most current approaches is that the proposed approach explicitly relates the risk of each specific BMV accident type to its related flows. The methodology was demonstrated using a 4-year (1992-1995) data set collected from 115 signalized intersections in the Tokyo Metropolitan area. This data set contains BMV accident data, bicycle flow data, motor vehicle flow data, traffic control data, and geometric data for each intersection approach. For each BMV risk model, an independent explanatory variable set was chosen according to the characteristics of the accident type. Three negative binomial regression models (one corresponding to each BMV accident type) were estimated using the maximum likelihood method. The coefficient value and its significance level were estimated for each selected variable. The negative binomial dispersion parameters for all the three models were significant at 0.01 levels. This supported the choice of the negative binomial regression over the Poisson regression for the quantitative analyses in this study.     

Assessing Performance Risk for Complex Product Development: A Simulation‐Based Model

Assessing and managing performance risk is important for complex product development projects. The performance risk of complex product development can be defined as the probability failing to achieve desired performance level subject to cost and time constraints. This article presents a simulation model based on the graphical evaluation and review technique (GERT) and analyzes the complex product development project using simulation data. The model computes the probability distribution of project duration and cost in a GERT network with multifeedback branches considering activities rework that is caused by probabilistic failure to meet the planned design objective. A simulation strategy is designed to assess the performance risk in both subsystems and system development. With the performance risk assessment methodology, project managers can make a better decision and then minimize the performance risk of product development project. Finally, an obstacle clearance armored vehicle development project is used to illustrate the effectiveness of the proposed model. Copyright © 2012 John Wiley & Sons, Ltd.     

Graphical tool for comparing ratios proposed in the literature on traffic accidents

Investigators of accidents use accident rates and ratios to measure risk of being involved in a traffic accident. This paper uses two examples to present a graphical technique, that summarizes scatters of points by ellipses, to help describe the accident involvement of drivers. The first appendix tells how the technique works and the second appendix summarizes other types of ratios used in the literature. The paper discusses how the technique may tie together ratios in the literature on accidents and ageing of drivers.     

Disaggregate model of highway accident occurrence using survival theory

The analysis of discrete accident data and aggregate exposure data frequently necessitates compromises that can obscure the relationship between accident occurrence and potential causal risk components. One way to overcome these difficulties is to develop a model of accident occurrence that includes accident and exposure data at a mathematically consistent disaggregate level. This paper describes the conceptual and mathematical development of such a model using principals of survival theory. The model predicts the probability of being involved in an accident at time t given that a vehicle has survived until that time. Several alternative functional forms are discussed including additive, proportional hazards and accelerated failure time models. Model estimation is discussed for the case in which both accident and nonaccident trips are included and for the case with only accident data. As formulated, the model has the distinct advantage of being able to consider accident and exposure data at a disaggregate level in an entirely consistent analytic framework. A conditional accident analysis is undertaken using truck accident data obtained from a major national carrier in the United States. Model results are interpretable and generally reasonable. Of particular interest is that segmenting accidents in several categories yields very different sets of significant parameters. Driver service hours seemed to most strongly effect accident risk: regularly scheduled drivers who take frequent trips are likely to have a reduced risk of an accident, particularly if they have a longer (greater than eight) number of hours off-duty just prior to a trip.     

Commentary on statistical issues underlying the evaluation of a social policy change on motor vehicle accident involvement

This commentary addresses the statistical issues which are involved in assessing whether a causal interpretation can be given to an association between a change in social policy such as lowering the legal age for drinking alcohol and a change in the nature of accident involvement frequency for a particular sub-population of drivers like 18–20 year olds. In this regard, two methodological concerns are emphasized. The first is the resolution of a possible paradox in the nature of an association in the sense that individuals with a greater tendency to accident involvement may also have a greater tendency to alcohol usage so that their alcohol related accident involvement is coincidental rather than causal. For this setting, alcohol would be an “after the fact” correlate with accident involvement as opposed to a possible cause. Similarly, the second concern is the role of population exposure as an explanation for the change in alcohol related accident frequency for a particular age group. Since availability naturally increases the size of the population at risk for such accidents, the corresponding accident frequency may similarly increase even though the corresponding accident rates may have remained unchanged or perhaps even decreased. For these reasons, although associations with change in social policy are definitely of general interest, their interpretation in a causal sense should be viewed with substantial caution until the implications of competing explanations are reconciled. This assertion, however, does not mean that such associations are not useful for policy purposes, but rather that they lack sufficiently targeted statistical validity to be the primary basis for such change. In other words, if there is a strong rationale on economic, political, or social grounds for a policy change, then data presenting meaningful associations which are compatible with it can often play an important supportive role for it.     

ARAMIS project: a comprehensive methodology for the identification of reference accident scenarios in process industries

In the frame of the Accidental Risk Assessment Methodology for Industries (ARAMIS) project, this paper aims at presenting the work carried out in the part of the project devoted to the definition of accident scenarios. This topic is a key-point in risk assessment and serves as basis for the whole risk quantification.

The first result of the work is the building of a methodology for the identification of major accident hazards (MIMAH), which is carried out with the development of generic fault and event trees based on a typology of equipment and substances. The term “major accidents” must be understood as the worst accidents likely to occur on the equipment, assuming that no safety systems are installed.

A second methodology, called methodology for the identification of reference accident scenarios (MIRAS) takes into account the influence of safety systems on both the frequencies and possible consequences of accidents. This methodology leads to identify more realistic accident scenarios. The reference accident scenarios are chosen with the help of a tool called “risk matrix”, crossing the frequency and the consequences of accidents.

This paper presents both methodologies and an application on an ethylene oxide storage.     

结构向量自回归模型因果图的信息论辨识方法

由观测数据确定变量间的因果关系是时间序列分析的重要内容.本文利用图模型方法研究结构向量自回归模型变量间的因果关系,通过时间序列因果图的建立将问题转化为时间序列因果图结构的辨识.基于信息论方法提出了因果性定向的三步准则,利用关联积分估计互信息和条件互信息.模拟结果显示本方法能更有效地辨识结构向量自回归模型因果图的因果结构.     

Using Control Charts to Monitor the Performance of Large-Scale Systems

ABSTRACT

The initial goal of this project was to provide Alcan Rolled Products Company of Kingston, Ontario, with control charts to monitor its new Paint Line Three (PL3) aluminium coil coating facility. As the project progressed, however, it became apparent that the following factors made PL3 ill-suited to traditional statistical quality control methods: the inexact nature of the coil coating science, the level of process interdependence and complexity of the operation, and the economic and technical infeasibility of installing sophisticated measuring devices at all stages of the coating line.

Consequently, an effort was made to model the entire system on a macroscopic, rather than a microscopic, level and to use the results of qualitative lab tests to make inferences about the paint line's process capability. To accomplish this, the length of scrap produced by every lot processed in a 1-year period was used to create a statistical model of the of the frequency and severity of the two most costly quality shortcomings: surface defects and coating problems.

This article outlines the methodology used and demonstrates how a complex phenomenon such as scrap creation can be modeled by using a combination of well-known statistical distributions. The statistical model can be used to transform raw data into meaningful graphical representations of large-scale system performance.     

The evaluation of effects on driver behavior and accidents of concentrated general enforcement on interurban roads in Israel

The study deals with the evaluation of the effects of a new deployment of the National Traffic Police in Israel--a concentrated general enforcement on 700 km of interurban roads, which contain 60% of all rural accidents and about half of the severe accident locations. The enforcement project began in April 1997 and lasted for 1 year, aiming at a 10% reduction of severe accidents on those roads. The evaluation study consisted of three main parts: (a) monitoring of everyday police operations on the project roads; (b) periodic evaluation of the project's influence on drivers' behavior and attitudes; and (c) the evaluation of accident changes within the project area, at the end of the project year. Speed measurements, performed before the police project launch, demonstrated that, depending on road type, up to 85% of drivers violated the rural road speed limits. Two rounds of field observations, before and during the project, took place at 32 sites throughout the project area and comprised eight behavior types: turning performance; signaling while turning; compliance with 'stop' and 'yield' signs; safety belt use by drivers and front-seat passengers, at the intersections; 'keeping to the right' on dual carriageway roads; and 'not-crossing of the white separating line' at single carriageway roads. A comparison revealed a general reduction in violation rates of most behaviors, during-the-project, except for compliance with 'stop' signs and signaling. Concurrently, two rounds of driver surveys were performed which examined the project's influence on the drivers' attitudes towards the police enforcement effectiveness and risk connected with traffic rule violations. An improvement was identified in perceived general level of police activity, during the project, whereas a lower or similar risk of apprehension was attributed by drivers to most specific violations of the traffic rules. Three groups of indices of police activity were estimated monthly during the study: inputs; outputs; and efficiency indices, which characterized the police presence on the project roads, the citations produced and the usage rates of the vehicle fleet and the enforcement tools. Analyzing these indices, two periods in the project performance were determined, and three criteria were proposed to subdivide the project roads into two groups, according to the level of enforcement intensity. Both periods and enforcement levels, as well as geographic zone, were accounted for in the analysis of accident trends on the project roads. For the accident analysis, a statistical model combining the odds ratio and longitudinal methods, was developed and applied to the severe accident counts, all injury accidents and severe casualties. A statistically significant reduction in severe accidents and severe casualties, as opposed to the comparison group, was found on the highly enforced roads in the center of the country. At the same time, since the police project began, an increase in accidents was observed on most interurban roads, including the northern parts of the project area. However, it was noted that in four of the five project road groups the mean value of the odds ratio was much less than one. Thus, although the enforcement project did not attain its full purpose, it seemed to be a deterrent factor for the increasing accident trend that appeared that year on the interurban roads. The findings pointed out that the National Traffic Police did not exhaust its potential in the project's performance and needs more flexible enforcement and deployment tactics.